Detecting Negative Emotions During Real-Life Driving via Dynamically Labelled Physiological Data

Abstract

Driving is an activity that can induce significant levels of negative emotion, such as stress and anger. These negative emotions occur naturally in everyday life, but frequent episodes can be detrimental to cardiovascular health in the long term. The development of monitoring systems to detect negative emotions often rely on labels derived from subjective self-report. However, this approach is burdensome, intrusive, low fidelity (i.e. scales are administered infrequently) and places huge reliance on the veracity of subjective self-report. This paper explores an alternative approach that provides greater fidelity by using psychophysiological data (e.g. heart rate) to dynamically label data derived from the driving task (e.g. speed, road type). A number of different techniques for generating labels for machine learning were compared: 1) deriving labels from subjective self-report and 2) labelling data via psychophysiological activity (e.g. heart rate (HR), pulse transit time (PTT), etc.) to create dynamic labels of high vs. low anxiety for each participant. The classification accuracy associated with both labelling techniques was evaluated using Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). Results indicated that classification of driving data using subjective labelled data (1) achieved a maximum AUC of 73%, whilst the labels derived from psychophysiological data (2) achieved equivalent performance of 74%. Whilst classification performance was similar, labelling driving data via psychophysiology offers a number of advantages over self-reports, e.g. implicit, dynamic, objective, high fidelity.

Determination of Optimal Heart Rate Variability Features Based on SVM-Recursive Feature Elimination for Cumulative Stress Monitoring Using ECG Sensor

“…Previous studies have similarly proposed optimal HRV features for stress monitoring [10,11,12,13,14,15,16]. However, most of those studies considered a singular exposure to a specific stressor.…”

“…In several studies, a feature selection approach such as the filter [13,14] and wrapper method [13,15,16] was used for determining optimal HRV features. Aigrain et al [15] evaluated the predictive power of various multimodal features by investigating the composition of the best feature subset and showed that the HR values (maximum and variation) and the amplitude of HR (maximum, mean, and variation) provided the best prediction among features related to ECGs.…”

A Psychophysiological Model of Firearms Training in Police Officers: A Virtual Reality Experiment for Biocybernetic Adaptation

“…A custom-made version of the conventional Tetris was adapted to create real-time adjustments of the game difficulty in three levels by using a SVM classifier that processed signals from skin response, blood volume and EEG (Chanel et al, 2011). Furthermore, learning-based classifiers have also been used to detect high and low anxiety in drivers from ECG and accelerometer data (Dobbins and Fairclough, 2018), or to create a virtual driving platform to maximize engagement in people with autism spectrum disorder (Bian et al, 2019). Applications for airplane pilots used classifiers to identify features from EEG and skin response signals that can model the users in scenarios of attention-related human performance limiting states (Harrivel et al, 2016) or to find relationships of cardiovascular features with psychophysiological stress while performing piloting maneuvers (Hanakova et al, 2017).…”

The Design of an Automated System for the Analysis of the Activity and Emotional Patterns of Dogs with Wearable Sensors Using Machine Learning

“…Although comparing the emotional model of human beings with an emotional model of animals is not perfect, in this paper, they were compared to check the feasibility and the effectiveness of the model. The emotion detection models implemented in the human considered the physiological signals as the primary input for the identification of emotional state [46,47]; those data were missing in our emotion detection model. One of the findings was that the relationship between body language pattern and emotional state described by [48], could be compared to our model because tail wagging is considered a decoded version of body language [49].…”